ACL [1]} is an unsupervised method and only uses the total count of unique actions that appear in the video, but it still achieves a comparable performance with respect to some of the weakly-supervised methods such as 3C-Net [2]}. Gong et al. [3]} is a self-supervised method that attained the state-of-the-art results on ActivityNet-1.2 among methods with limited supervision, confirming the advantageous of self-supervised learning. The recent state-of-the-art weakly supervised methods such as D2-Net [4]} achieved comparable performance to the semi-supervised methods of Ji et al [5]} and TTC-Loc [6]}. This is interesting specially because D2-Net [4]} does not use temporal annotation of actions at all while Ji et al [5]} and TTC-Loc [6]} use temporal annotations at least for a small percentage of videos in the dataset.
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